Amazon SageMaker
Amazon SageMaker is a fully managed machine learning platform that enables developers and data scientists to build, train, and deploy machine learning models at scale. SageMaker removes the heavy lifting from each step of the machine learning process, providing built-in algorithms, managed Jupyter notebooks, distributed training, automatic model tuning, and one-click deployment to production endpoints with auto-scaling.
Reference-quality API operations across every facet — a rich contract, published governance, transparent operations, and machine-readable commercial terms.
API Evangelist profiles Amazon SageMaker the way a machine reads it — 82 machine-readable artifacts across 9 APIs, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Amazon SageMaker scores 76.4/100 (exemplar), with a separate agent-readiness read of 45/100 (agent ready). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Amazon SageMaker
Each block below is one kind of artifact we hold for Amazon SageMaker. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
APIs 9
Each API is captured as its own OpenAPI definition — every operation, parameter, and response. This is the single most useful machine-readable description of what an API does, and it's what lets us score, lint, mock, and generate against it without asking the provider for anything.
Individual APIs this provider publishes, each with its own machine-readable definition.
Amazon SageMaker Runtime API
The Amazon SageMaker AI runtime API for invoking deployed model endpoints to get real-time inference predictions.
Amazon SageMaker Feature Store Runtime API
Data plane API operations for the Amazon SageMaker Feature Store supporting put, delete, and retrieve operations for ML features.
Amazon SageMaker Metrics Service API
Data plane API operations for Amazon SageMaker Metrics for putting and retrieving metrics related to training runs.
Amazon SageMaker Geospatial API
APIs for creating and managing Amazon SageMaker geospatial capabilities including earth observation jobs and vector enrichment jobs.
Amazon SageMaker Edge Manager API
SageMaker Edge Manager dataplane service for communicating with active edge agents running ML models on edge devices.
Amazon SageMaker Endpoints API
Operations for managing SageMaker endpoints.
Amazon SageMaker Models API
Operations for managing SageMaker models.
Amazon SageMaker Notebook Instances API
Operations for managing SageMaker notebook instances.
Amazon SageMaker Training Jobs API
Operations for managing SageMaker training jobs.
Scroll within the panel for all 9 ·
Postman Collections 1
A runnable collection turns the contract into something a developer can execute in seconds. We profile them because the fastest way to trust an API is to make a real call against it.
Ready-to-run Postman collections for exercising this provider's APIs.
Amazon SageMaker API
POSTMANOpen Collections 1
Open, tool-agnostic collections carry the same runnable value as Postman without locking you to one client — the portable, forkable form of the same exercise.
Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Amazon SageMaker API
OPEN COLLECTIONArazzo Workflows 8
Real integrations are rarely a single call. Arazzo describes the multi-step sequences — auth, then create, then confirm — so both a human and an agent can follow the choreography, not just the endpoints.
Multi-step API workflows described with the Arazzo specification.
Amazon SageMaker Audit Endpoint Fleet
List hosted endpoints and describe the most recently created one in detail.
ARAZZOAmazon SageMaker Deploy Existing Model
Verify an existing model, build an endpoint configuration for it, create an endpoint, and poll it to service.
ARAZZOAmazon SageMaker Deploy Model to Endpoint
Create a model, build an endpoint configuration, launch an endpoint, and poll it until it is in service.
ARAZZOAmazon SageMaker Inventory Models
List registered models and describe the most recently created one in detail.
ARAZZOAmazon SageMaker Provision Notebook Instance
Create a SageMaker notebook instance and poll it until it is in service.
ARAZZOAmazon SageMaker Register Latest Completed Training
Find the most recent completed training job, read its artifacts, and register a model from them.
ARAZZOAmazon SageMaker Train Model and Poll Job
Start a SageMaker training job and poll its status until it reaches a terminal state.
ARAZZOAmazon SageMaker Train Then Deploy
Train a model to completion, then register it from the produced artifacts and stand up a hosted endpoint.
ARAZZOScroll within the panel for all 8 ·
GraphQL 1
Where a provider ships GraphQL, the schema is the contract. We profile it alongside the REST surface so the whole interface is legible in one place.
GraphQL schemas published by this provider.
Amazon SageMaker GraphQL Schema
This GraphQL schema provides a conceptual graph representation of the [Amazon SageMaker REST API](https://docs.aws.amazon.com/sagemaker/latest/APIReference/). SageMaker is a ful...
GRAPHQLPricing Plans 1
Pricing is part of the interface. Machine-readable plans tell you what a tier costs and includes before you commit — one of the six things the Kin Score reads for commercial clarity.
Published pricing tiers and plan structures.
Rate Limits 1
Rate limits are the difference between a demo that works and a production integration that doesn't fall over. Publishing them is an operational-transparency signal — and a hard requirement for any agent that plans its own throughput.
Documented rate limits and quota policies.
Amazon Sagemaker Rate Limits
RATE LIMITSFinOps 1
Cost, billing, and metering signals let a buyer model the financial operations of an API before it's live. We profile them for the same reason we profile pricing: the money is part of the contract.
Cost, billing, and metering signals for API financial operations.
Amazon Sagemaker Finops
FINOPSFeatures 13
The notable capabilities this provider advertises, captured as structured features so they can be searched and compared instead of read one landing page at a time.
Notable capabilities this provider offers.
SageMaker Studio
Fully integrated development environment for ML work with notebooks, debugging, and experiment tracking.
SageMaker HyperPod
Purpose-built infrastructure for distributed training that reduces foundation model training time by up to 40%.
SageMaker JumpStart
Hub providing access to foundation models, pre-built algorithms, and one-click deployment.
SageMaker Autopilot
Automated model creation with complete visibility and transparency.
SageMaker Canvas
No-code visual interface for creating ML models without writing code.
SageMaker Feature Store
Store, share, and manage features for machine learning models.
SageMaker Data Wrangler
Data preparation tool that reduces transformation workflow time significantly.
SageMaker Ground Truth
Incorporates human feedback throughout the ML lifecycle for data labeling.
SageMaker Pipelines
Purpose-built CI/CD service for machine learning workflows.
SageMaker Model Monitor
Automatically detects concept drift and data quality issues in deployed models.
SageMaker Clarify
Provides machine learning explainability and bias detection.
SageMaker Experiments
Streamlines tracking and management of ML experiments.
ML Governance
Access controls and transparency across the full ML lifecycle with audit trails.
Scroll within the panel for all 13 ·
Semantic Vocabularies 1
JSON-LD contexts give the data shared meaning across APIs. We profile them because semantics are what let a machine reconcile 'customer' here with 'customer' somewhere else.
JSON-LD contexts and semantic vocabularies used across these APIs.
Amazon Sagemaker Context
JSON-LDSpectral Rules 2
Governance rulesets we run against this provider's specs — the automated checks behind parts of the score. Profiling them makes the quality bar explicit and re-runnable, not a matter of opinion.
Amazon SageMaker API Rules
SPECTRALAmazon SageMaker API Rules
SPECTRALJSON Schema 7
Standalone JSON Schema definitions describe the data models behind the API. We profile them so the shapes are validatable on their own — useful long after a single request is forgotten.
Standalone JSON Schema definitions for this provider's data models.
Endpoint
JSON SCHEMAModel
JSON SCHEMANotebookInstance
JSON SCHEMANotebookInstance
JSON SCHEMATag
JSON SCHEMATrainingJob
JSON SCHEMATrainingJob
JSON SCHEMAScroll within the panel for all 7 ·
JSON Structure 6
JSON Structure captures the data shapes in a form built for tooling — a complement to JSON Schema that keeps the model machine-legible.
JSON Structure definitions describing this provider's data shapes.
Amazon Sagemaker Endpoint Structure
JSON STRUCTUREAmazon Sagemaker Model Structure
JSON STRUCTUREAmazon Sagemaker Notebook Instance Structure
JSON STRUCTUREAmazon Sagemaker Structure
JSON STRUCTUREAmazon Sagemaker Tag Structure
JSON STRUCTUREAmazon Sagemaker Training Job Structure
JSON STRUCTUREExamples 18
Real request and response payloads are what turn a spec from abstract into obvious — and they're one of the twelve things an agent needs to call an API correctly on the first try.
Example request and response payloads for these APIs.
Amazon Sagemaker Tag Example
EXAMPLEScroll within the panel for all 18 ·
Security Posture 3
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Agentic Access 1
An x-agentic-access contract marks which operations are safe for an agent to run on its own and which need a human in the loop. It is the difference between an API an agent can use and one it can use safely.
Recommended x-agentic-access execution contracts for AI agents.
Use Cases 8
What developers actually build with this provider — captured so the catalogue answers 'what is this for', not just 'what does this expose'.
What developers build with this provider.
Generative AI Applications
Build custom generative AI applications using proprietary data with foundation model fine-tuning.
ML Model Development
Train and deploy ML models across the entire machine learning lifecycle from exploration to production.
Data Analytics
Query and analyze data across unified sources with built-in SQL analytics and data processing.
Enterprise AI Governance
Manage data and AI artifacts with fine-grained security controls and compliance tooling.
Computer Vision
Build and deploy computer vision models for image classification, object detection, and segmentation.
Natural Language Processing
Train and deploy NLP models for text classification, entity recognition, and language generation.
Fraud Detection
Build real-time fraud detection models with low-latency inference endpoints.
Predictive Maintenance
Deploy ML models on edge devices for predictive maintenance use cases.
Scroll within the panel for all 8 ·
Resources
Every other property we hold for Amazon SageMaker — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Get Started 4
Portal, sign-up, and the first successful call
Documentation 3
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Design & Contract 16
Pagination, idempotency, versioning, errors, and events
Scroll within the panel for all 16 ·
Build 12
SDKs, sample code, and the tooling you integrate with
Scroll within the panel for all 12 ·
Access & Security 5
Authentication, authorization, and security posture
Learn 2
Tutorials, courses, talks, and written guidance
Operate 4
Status, limits, changes, and where to get help
Commercial 3
Pricing, plans, and the legal terms of use
Company 1
The organization behind the API
Other 1
Properties that don't map to a standard resource type
← All providers · Data indexed from github.com/api-evangelist/amazon-sagemaker · machine-readable index on apis.io